Abstract

Numerous applications require the sharing of data from each node on a network with every other node. In the case of Connected and Autonomous Vehicles (CAVs), it will be necessary for vehicles to update each other with their positions, manoeuvring intentions, and other telemetry data, despite shadowing caused by other vehicles. These applications require scalable, reliable, low latency communications, over challenging broadcast channels. In this article, we consider the allcast problem, of achieving multiple simultaneous network broadcasts, over a broadcast medium. We model slow fading using random graphs, and show that an allcast method based on sparse random linear network coding can achieve reliable allcast in a constant number of transmission rounds. We compare this with an uncoded baseline, which we show requires O(log(n)) transmission rounds. We justify and compare our analysis with extensive simulations.
Original languageEnglish
Number of pages5
Publication statusPublished - 27 Sep 2019
Event57th Annual Allerton Conference on Communication, Control, and Computing - Allerton Park and Retreat Center, 515 Old Timber Road, Monticello, United States
Duration: 24 Sep 201927 Sep 2019
Conference number: 57
https://allerton.csl.illinois.edu/

Conference

Conference57th Annual Allerton Conference on Communication, Control, and Computing
CountryUnited States
CityMonticello
Period24/09/1927/09/19
Internet address

Keywords

  • Sparse RLNC
  • CAV
  • Allcast
  • V2V
  • gossip

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  • Cite this

    Graham, M. A., Ganesh, A. J., & Piechocki, R. J. (2019). Sparse random linear network coding for low latency allcast. Paper presented at 57th Annual Allerton Conference on Communication, Control, and Computing, Monticello, United States.